Phayuth Yonrith
Papers
2
Total Citations
16
H-Index
1
About
Phayuth Yonrith is a robotics researcher whose work focuses on the intersection of computer vision and autonomous manipulation, particularly for agricultural and industrial applications. His most cited paper, "Enhancing detection performance for robotic harvesting systems through RandAugment" (2023, 15 citations), introduces a data augmentation strategy that significantly improves the robustness of object detection models in dynamic, unstructured environments—a critical step toward reliable automated harvesting. This contribution addresses a key bottleneck in precision agriculture, where variable lighting and occlusions often degrade perception accuracy. In his more recent work, "Robot Path Planning With Grasping Pose Flexibility Incorporating Local Gap Sampling Approach" (2025), Yonrith advances motion planning by integrating grasp flexibility into path optimization, enabling robots to adapt their end-effector orientations for more efficient and dexterous manipulation. Though early in his career, his research demonstrates a clear trajectory toward bridging perception and action in real-world robotic systems. With a growing citation record and a focus on practical deployment challenges, Yonrith is establishing himself as a promising voice in the fields of agricultural robotics and intelligent manipulation.
Research Focus
Key Achievements
Top Papers
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- 2